Machine Learning -- data visualizations--PCA (heart disease data)

最新推荐文章于 2023-07-22 07:00:27 发布

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最新推荐文章于 2023-07-22 07:00:27 发布 · 588 阅读

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该章节通过PCA进行数据可视化，将13维心脏病数据降至2维。标准化数据后，发现前两个主成分解释了47.22%的方差，但大量信息丢失，不适合项目需求。最终决定在后续分析中使用全部13个特征。

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In this chapte, I will use PCA for data visualization. Visualizing 2 or 3 dimensional data is not that challenging, however, here we get 13 features.
Now, I will use PCA to reduce that 13 dimensional data into 2 dimensions so that you can plot and hopefully understand the data better.

Step1: Standardize the Data

PCA is effected by scale so you need to scale the features in your data before applying PCA. Use StandardScaler to help you standardize the dataset’s features onto unit scale (mean = 0 and variance = 1) which is a requirement for the optimal performance of many machine learning algorithms.

## load data
trainSet = pd.read_csv("clevelandtrain.csv")
testSet = pd.read_csv("clevelandtest.csv")

xtrain = (trainSet.drop(["heartdisease::category|0|1"], axis=1)).iloc[:,:].values  # (152, 13)
ytrain = trainSet["heartdisease::category|0|1"].iloc[:].values                     # (152,)

xtest = (testSet.drop(["heartdisease::category|0|1"], axis=1)).iloc[:,:].values    # (145, 13)
ytest = testSet["heartdisease::category|0|1"].iloc[:].values                       # (145,)

print("the first 4 raw data is:\n"<